Kalodata MCP Server and API by Cotlet

Export creator list in Kalodata

kalodata_export_creator_list

Read-onlyIdempotent

Export creator list in the user's own Kalodata account from ChatGPT, Claude or any AI agent. Returns how to do it in Kalodata step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Kalodata Cloud Browser. Kalodata's official API: Yes, Kalodata has an Open API with ranking and detail endpoints for Category, Shop, Creator, Product, Video and Livestream data, billed per credit (checked 5 Nov 2026).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "kalodata_export_creator_list"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A3.9/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description usefully adds that the tool performs no actions itself ('opens nothing by itself') and that the return is a step-by-step procedure, which is meaningful behavioral context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The first two sentences are front-loaded and earn their place. The third sentence about Kalodata's official Open API is tangential to what this tool does and could distract or mislead an agent into thinking this tool calls the API rather than returning UI instructions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must describe the return; it does so adequately ('step by step (screens, buttons, fields)'). For a zero-parameter guidance tool whose annotations cover safety, this is nearly complete, though the return format (text vs. structured) is not specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is no parameter semantics to explain. The baseline of 4 applies; the description does not need to compensate for any schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the verb and resource: it returns step-by-step instructions (screens, buttons, fields) for exporting a creator list in Kalodata, and explicitly says it opens nothing itself. The opening line ('Export creator list in the user's own Kalodata account') slightly overstates the action before the corrective sentence, but the core purpose is distinguishable from the cotlet_browser_* siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear routing: call this tool to get the procedure, then perform it with the cotlet_browser_* tools in the user's Kalodata Cloud Browser. It does not state when NOT to use it (for example, when the user already knows the steps or wants the API route), so it stops short of explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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